1. Field of Invention
This invention relates to the detection of linked events.
2. Description of Related Art
Link detection systems determine whether stories in news and radio broadcasts, newspaper articles and journal reports are about the same event. Link detection systems differ from conventional categorization systems in that the events to be detected occur frequently but may not have been seen before. Thus, effective link detection systems must devise strategies for learning from new input or must determine ways of characterising the input independent of the particular event.
Conventional link detection systems have attempted to improve link detection by refining or developing new similarity metrics such as the weighted sum, language modeling, Kullback-Leibler and cosine similarity metrics. Other conventional systems have refined the application of conventional term frequency-inverse document frequency models. For example, some conventional link detection systems continuously adapt and apply models based on source-pair classes. Some other conventional link detection systems have attempted to improve detection results using source-specific similarity thresholds and two level schema for topic conditioned first story detection. Still other conventional link detection systems have attempted to improve link detection performance by training support vector machines with term frequency-inverse document frequency information. However, these systems have not performed well.